Abstract
Cloud computational platforms today are very promising for execution of scientific applications since they provide ready to go infrastructure for almost any task. However, complex tasks, which contain a large number of interconnected applications, which are usually called workflows, require efficient tasks scheduling in order to satisfy user defined QoS, like cost or execution time (makespan). When QoS has some restrictions – limited cost or deadline – scheduling becomes even more complicated. In this paper we propose heuristic algorithm for scheduling workflows in hard-deadline constrained clouds – Levelwise Deadline Distributed Linewise Scheduling (LDD-LS) – which, in combination with implementation of IC-PCP algorithm, is used for initialization of proposed metaheuristic algorithm – Cloud Deadline Coevolutional Genetic Algorithm (CDCGA). Experiments show high efficiency of CDCGA, which makes it potentially applicable for scheduling in cloud environments.
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